You can deploy this model and start earning money today!

JIIOV/
RoboChallenge-CVPR-2026

Views

No views yet

reproduce steps

download model, python download_checkpoints.py
edit submission ids in Makefile
eval a robot, make eval_pi05_{robot_type}

CVPR Pi0.5 Multi-Task Example

This branch is a CVPR on-robot inference example for Pi0.5 multi-task policies. There is one checkpoint per robot platform; you can evaluate all tasks supported on that platform with the same model, by changing the task prompt (and episode data) only. Supported robot types in this codebase (see demo.py / test.py): dosw, aloha, arx5, ur5.

Core entrypoints:

test.py — Connects to the local mock robot server for quick debugging.
demo.py — RoboChallenge official evaluation (requires user_token and submission_id).

Quick start

1. Installation

bash

uv venv
source .venv/bin/activate   # if your uv venv uses another path, activate that instead
uv pip install -e ./openpi
uv pip install pytest
uv pip install -r requirements.txt

2. Download the example model

Download the Pi0.5 multi-task checkpoint from RoboChallenge on Hugging Face. One checkpoint per embodiment covers all tasks on that robot; switch tasks via --prompt only.

Platform	`robot_type` (in `test.py` / `demo.py`)	model_name	Mock `ROBOT_TAG` (mock test section)
W1	`dosw`	`table30v2_multitask_baseline_w1`	`w1`
Aloha	`aloha`	`table30v2_multitask_baseline_aloha`	`aloha`
ARX5	`arx5`	`table30v2_multitask_baseline_arx5`	`arx5`
UR5	`ur5`	`table30v2_multitask_baseline_ur5`	`ur5`

3. Run the mock test

Edit mock_server/mock_settings.py: set ROBOT_TAG and RECORD_DATA_DIR (only one active pair). With the bundled sample data (paths relative to repo root):
- ROBOT_TAG='aloha', RECORD_DATA_DIR='../20260413/aloha/pack_the_toothbrush_holder'
- ROBOT_TAG='w1', RECORD_DATA_DIR='../20260413/w1/sweep_the_trash' → run test.py with --robot_type dosw
- ROBOT_TAG='ur5', RECORD_DATA_DIR='../20260413/ur5/arrange_fruits'
- ROBOT_TAG='arx5', RECORD_DATA_DIR='../20260413/arx5/hang_the_cup'
Terminal 1 — start the mock server:

bash
1cd mock_server 2python3 mock_robot_server.py

For debug mode (state includes a consecutive action_chunk starting from current frame, up to 30 frames total), start with:

bash
1cd mock_server 2python3 mock_robot_server.py --debug
Terminal 2 — from the repo root:

bash
1python3 test.py \ 2 --checkpoint /path/to/checkpoint_dir \ 3 --prompt "task instruction in natural language(match the format used in training)" \ 4 --robot_type dosw

Use exactly one of --robot_type dosw | aloha | arx5 | ur5 so it matches ROBOT_TAG.

Adjust --action_type, --duration, --image_size, etc. as needed (see test.py).

4. Run the demo

After you have a submission and are in the assigned evaluation window, run demo.py.

bash

python3 demo.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/w1_checkpoint_dir \
  --prompt "task instruction in natural language(match the format used in training)" \
  --action_type joint \
  --image_size "640x480" \
  --robot_type dosw

Aloha

bash

python3 demo.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/aloha_checkpoint_dir \
  --prompt "task instruction in natural language(match the format used in training)" \
  --action_type joint \
  --image_size "640x480" \
  --robot_type aloha

ARX5

bash

python3 demo.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/arx5_checkpoint_dir \
  --prompt "task instruction in natural language(match the format used in training)" \
  --action_type leftjoint \
  --image_size "1280x720" \
  --robot_type arx5

UR5

bash

python3 demo.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/ur5_checkpoint_dir \
  --prompt "task instruction in natural language(match the format used in training)" \
  --action_type leftjoint \
  --image_size "640x480" \
  --robot_type ur5

CVPR DM0 Multi-Task Example

This section covers using the DM0 policy backend. The entry points are:

test.py — Mock robot server for local debugging (--policy_type dm0).
eval.py — RoboChallenge official evaluation.

Quick start (DM0)

1. Installation

Same as Pi0.5. See the Installation section above.

2. Run the mock test (DM0)

Configure mock_server/mock_settings.py the same way as the Pi0.5 section above.
Terminal 1 — start the mock server:

bash
1cd mock_server 2python3 mock_robot_server.py
Terminal 2 — from the repo root:

bash
1python3 test.py \ 2 --policy_type dm0 \ 3 --checkpoint /path/to/dm0_checkpoint_dir \ 4 --prompt "task instruction in natural language" \ 5 --robot_type arx5

Add --save_state to save every state observation to eval_data/:

bash
1python3 test.py \ 2 --policy_type dm0 \ 3 --checkpoint /path/to/dm0_checkpoint_dir \ 4 --prompt "task instruction in natural language" \ 5 --robot_type arx5 \ 6 --save_state

3. Run the official evaluation (DM0)

Use eval.py instead of demo.py for the DM0 backend.

bash

python3 eval.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/dm0_w1_checkpoint_dir \
  --prompt "task instruction in natural language" \
  --action_type joint \
  --image_size "640x480" \
  --robot_type dosw

Aloha

bash

python3 eval.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/dm0_aloha_checkpoint_dir \
  --prompt "task instruction in natural language" \
  --action_type joint \
  --image_size "640x480" \
  --robot_type aloha

ARX5

bash

python3 eval.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/dm0_arx5_checkpoint_dir \
  --prompt "task instruction in natural language" \
  --action_type leftjoint \
  --image_size "1280x720" \
  --robot_type arx5

UR5

bash

python3 eval.py \
  --user_token <your_user_token> \
  --submission_id <your_submission_id> \
  --checkpoint /path/to/dm0_ur5_checkpoint_dir \
  --prompt "task instruction in natural language" \
  --action_type leftjoint \
  --image_size "640x480" \
  --robot_type ur5

Add --save_state to any of the above commands to save:

state observations: eval_data/{robot_id}/{date}-{time}/{job_id}/state_{seq}.pkl
posted actions: eval_data/{robot_id}/{date}-{time}/{job_id}/infer_{seq}.json

Original README.md

RoboChallengeInference

Project Structure

- RoboChallengeInference/
    - README.md
    - requirements.txt
    - demo.py
    - test.py  # Main test entry script
    - robot/
        - __init__.py
        - interface_client.py
        - job_worker.py
    - mock_server
        - mock_rc_robot.py
        - mock_robot_server.py
        - mock_settings.py
        - utils.py
    - utils/
        - __init__.py
        - enums.py
        - log.py
        - util.py

User Guide

1. Installation

bash

# Clone the repository and checkout the specified branch
git clone https://github.com/RoboChallenge/RoboChallengeInference.git
cd RoboChallengeInference

# (Recommended) Create and activate a virtual environment to avoid polluting your global Python environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

2. Checkout & Modification

bash

# Checkout
git checkout -b my-feature-branch
# Follow the instructions in demo.py to modify parameters and implement your custom inference logic based on DummyPolicy.
# The current task prompt will be passed into `DummyPolicy.run_policy(input_data, prompt=...)`.

3. Test

bash

# Open the mock_settings.py file and set the ROBOT_TAG and RECORD_DATA_DIR variables according to your robot and data directory requirements.
# Notes:
#   Only one pair of ROBOT_TAG and RECORD_DATA_DIR should be active at a time.
#   Ensure that the RECORD_DATA_DIR path matches the structure of your data folder.
#   You can find the appropriate ROBOT_TAG in your training data or on our website.
#   For the 20260413 CVPR package, you can use one of the following pairs:
#     ROBOT_TAG='aloha', RECORD_DATA_DIR='../20260413/aloha/pack_the_toothbrush_holder'
#     ROBOT_TAG='w1',    RECORD_DATA_DIR='../20260413/w1/sweep_the_trash'
#     ROBOT_TAG='ur5',   RECORD_DATA_DIR='../20260413/ur5/arrange_fruits'
#     ROBOT_TAG='arx5',  RECORD_DATA_DIR='../20260413/arx5/hang_the_cup'
#   RECORD_DATA_DIR also supports robot-level directory (e.g. '../20260413/ur5').
#   The mock server will auto-detect the task directory with meta/states/videos.
# Start the test service
cd mock_server
python3 mock_robot_server.py
# Use test.py for testing; it will automatically invoke the mock interface to help you debug your model
# Replace {your_args} with the actual parameters you want to test, for example: --checkpoint xxx.
# Run this in another shell at repo root.
python3 test.py {your_args}

4. Submit

Log in to RoboChallenge Web
Submit an evaluation request
On the "My Submission" page, you can view your submissions. Click "Detail" to see more information about a submission.
The Submission ID displayed on the details page will be required for the evaluation process. The program will automatically poll and select active runs under that submission.

5. Execute

Wait for a notification (on the website or via email) indicating that your task has been assigned.
Ensure the modified code from the previous steps is actively running during the assigned period.
Start the evaluation worker with:

python3 demo.py --user_token <your_user_token> --submission_id <your_submission_id> --checkpoint <your_checkpoint>

After the task is completed, the program will exit normally. If you encounter any issues or exceptions, please feel free to contact us.

6. Result

Once your task has been executed, you can view the results by visiting the "My Submissions" page on the website.

Key API Parameter Descriptions

This is the direct interface for the robot. The base URL is /api/robot/<id>/direct. For example, if the robot ID is 1, the full URL to get the state is /api/robot/1/direct/state.pkl.

Sync Clock

Endpoint: /clock-sync
Method: GET

Request Parameters

None

Response Example

json

{
  "timestamp": 0.0
}

Response Fields

Field	Type	Description
timestamp	float	unix timestamp on the robot

Get State

Endpoint: /state.pkl
Method: GET

Request Parameters

Parameter	Type	Required	Default	Description
width	integer	No	224	Width of the image
height	integer	No	224	Height of the image
image_type	list of str	Yes	None	Camera names. Only robot-specific `cam_*` keys listed below are supported. If you send unsupported keys, server returns `JSONResponse` error with valid options.
action_type	str	Yes	None	Control mode. Only robot-specific values listed below are supported. If you send unsupported values, server returns `JSONResponse` error with valid options.

Robot-specific image_type values and returned images keys:

Robot	Use this `image_type`	`images` keys returned
`aloha`	`cam_left_wrist`, `cam_right_wrist`, `cam_high`	`cam_left_wrist`, `cam_right_wrist`, `cam_high`
`w1`	`cam_left_wrist`, `cam_right_wrist`, `cam_high`	`cam_left_wrist`, `cam_right_wrist`, `cam_high`
`ur5`	`cam_global`, `cam_arm`	`cam_global`, `cam_arm`
`arx5`	`cam_global`, `cam_arm`, `cam_side`	`cam_global`, `cam_arm`, `cam_side`

Robot-specific action_type values:

Robot	Supported `action_type`
`aloha`	`joint`, `pos`, `leftjoint`, `leftpos`, `rightjoint`, `rightpos`
`w1`	`joint`, `pos`, `leftjoint`, `leftpos`, `rightjoint`, `rightpos`
`ur5`	`leftjoint`, `leftpos`
`arx5`	`leftjoint`, `leftpos`

Response Example

The response is a pickle file containing a dictionary with the following structure. The images keys depend on robot type:

aloha / w1

python

{
    "images": {
        "cam_left_wrist": b'PNG',
        "cam_right_wrist": b'PNG',
        "cam_high": b'PNG'
    }
}

ur5

python

{
    "images": {
        "cam_global": b'PNG',
        "cam_arm": b'PNG'
    }
}

arx (arx5)

python

{
    "images": {
        "cam_global": b'PNG',
        "cam_arm": b'PNG',
        "cam_side": b'PNG'
    }
}

Full response example (aloha, action_type=joint):

python

{
    "state": 'normal',
    "timestamp": 1774949968.382069,
    "pending_actions": 0,
    "action": [
        -0.0151, 0.0, 0.0, -0.0184, 0.0986, 0.0529, 0.0001,
         0.0113, 0.0, 0.0, -0.0079, 0.0962, 0.0298, 0.0
    ],
    "images": {
        "cam_left_wrist": b'PNG',
        "cam_right_wrist": b'PNG',
        "cam_high": b'PNG'
    }
}

Response Fields

Field	Type	Description
state	string	Robot state. Should be `normal` if the robot is operational. If the value is `fault` or `abnormal`, there is an issue with the robot. If the value is `size_none`, the request parameter `image_type` or `action_type` is missing.
timestamp	float	Unix timestamp on the robot
pending_actions	integer	Number of pending actions in the queue
action	list of float	Current robot joint or position values. If `action_type` in the request contains `joint`, the joint values will be returned. If it contains `pos`, the tool end positions will be returned. If it contains `left` or `right`, only the values for the left or right arm will be returned. If neither is specified, values for both arms will be returned. For example, if the robot is Aloha with two arm, the list consists with `[joints of left arm, gripper of left arm, joints of right arm, gripper of right arm]`. See the Robot specific Notes section for detailed information.
action_chunk	list of list of float	Optional. Only returned in mock server `--debug` mode. Contains consecutive action frames starting from current frame, with up to 30 frames total (including current frame). If fewer than 30 frames remain, returns all remaining frames only. Per-frame layout matches `action` under the same `action_type`.
images	dict	Dictionary of images. Only includes camera positions specified in the `image_type` request parameter.
images.cam_left_wrist	bytes	PNG image bytes for `aloha`/`w1`, if requested
images.cam_right_wrist	bytes	PNG image bytes for `aloha`/`w1`, if requested
images.cam_high	bytes	PNG image bytes for `aloha`/`w1`, if requested
images.cam_global	bytes	PNG image bytes for `ur5`/`arx`, if requested
images.cam_arm	bytes	PNG image bytes for `ur5`/`arx`, if requested
images.cam_side	bytes	PNG image bytes for `arx`, if requested

Post Action

Endpoint: /action
Method: POST

Request Parameters

Parameter	Type	Required	Default	Description
action_type	str	Yes	None	Control mode. Only robot-specific values listed above are supported. If you send unsupported values, server returns `JSONResponse` error with valid options.

The HTTP body should be a JSON object with the following structure:

json

{
  "actions": [
    [
      0.0,
      0.0
    ],
    [
      0.0,
      0.0
    ],
    [
      0.0,
      0.0
    ]
  ],
  "duration": 0.0
}

Field	Type	Description
actions	2D float list	Target joint or position values. If `action_type` in the request contains `joint`, the target values control the robot joints. If it contains `pos`, the tool end positions will be controlled. If it contains `left` or `right`, only the left or right arm will be controlled. If neither is specified, both arms will be controlled. The shape of the array is (number of actions, target values per action). For example, if you are using ALOHA and `action_type` is `joint`, then the shape of the actions array should be (N, 14): 6 joints and 1 gripper per arm, N is the number of steps your model infers. See the Robot specific Notes section for detailed information.
duration	float	Duration (second) per action

Response Example

json

{
  "result": "success",
  "message": ""
}

Response Fields

Field	Type	Description
result	string	Result of the request. Only `success` or `error` will be returned.
message	string	Reason for `error` result, if any. possible message: the robot is not running (fault or logging), the action shape is wrong, action queue is full, other exception

Robot specific Notes

Different robots have different action shapes and camera placement.

W1
- Dual-arm robot
- 7 DOF per arm (6 joints + 1 gripper)
- Joint control:
  - one arm(left or right): 7 numbers total: [6 joints, 1 gripper]
  - two arms: 14 numbers total: [left 6 joints, left 1 gripper, right 6 joints, right 1 gripper]
- Pose control
  - one arm(left or right): 8 numbers total: [x, y, z, quaternion(xyzw), gripper]
  - two arms: 16 numbers total: [left x, left y, left z, left quaternion(xyzw), left gripper, right x, right y, right z, right quaternion(xyzw), right gripper]
- 3 cameras: mounted on left/right arm, and on the top of the robot
Aloha
- Dual-arm robot
  - 7 DOF per arm (6 joints + 1 gripper)
  - Joint control:
    - one arm(left or right): 7 numbers total: [6 joints, 1 gripper]
    - two arms: 14 numbers total: [left 6 joints, left 1 gripper, right 6 joints, right 1 gripper]
  - Pose control
    - one arm(left or right): 8 numbers total: [x, y, z, quaternion(xyzw), gripper]
    - two arms: 16 numbers total: [left x, left y, left z, left quaternion(xyzw), left gripper, right x, right y, right z, right quaternion(xyzw), right gripper]
  - 3 cameras: mounted on left/right arm, and on the top of the robot
Arx5
- Single-arm robot
- 7 DOF (6 joints + 1 gripper)
- Joint control: 7 numbers total: [6 joints, 1 gripper]
- Pose control: 8 numbers total: [x, y, z, quaternion(xyzw), gripper]
- You must always use left in the action_type parameter, e.g., leftjoint or leftpos.
- 3 cameras: mounted on the arm, opposite to the arm, and on the right side of the arm
Ur5
- Single-arm robot
- 7 DOF (6 joints + 1 gripper)
- Joint control: 7 numbers total: [6 joints, 1 gripper]
- Pose control: 8 numbers total: [x, y, z, quaternion(xyzw), gripper]
- You must always use left in the action_type parameter, e.g., leftjoint or leftpos.
- 2 cameras: mounted on the arm, and opposite to the arm

Contact

For official inquiries or support, you can reach us via:

GitHub Issues: https://github.com/RoboChallenge/RoboChallengeInference/issues
Reddit: https://www.reddit.com/r/RoboChallenge/
Discord: https://discord.gg/8pD8QWDv
X (Twitter): https://x.com/RoboChallengeAI
HuggingFace: https://huggingface.co/RoboChallenge
GitHub: https://github.com/RoboChallenge
Support Email: support@robochallenge.ai

JIIOV/RoboChallenge-CVPR-2026

reproduce steps

CVPR Pi0.5 Multi-Task Example

Quick start

1. Installation

2. Download the example model

3. Run the mock test

4. Run the demo

CVPR DM0 Multi-Task Example

Quick start (DM0)

1. Installation

2. Run the mock test (DM0)

3. Run the official evaluation (DM0)

Original README.md

RoboChallengeInference

Project Structure

User Guide

1. Installation

2. Checkout & Modification

3. Test

4. Submit

5. Execute

6. Result

Key API Parameter Descriptions

Sync Clock

Request Parameters

Response Example

Response Fields

Get State

Request Parameters

Response Example

Response Fields

Post Action

Request Parameters

Response Example

Response Fields

Robot specific Notes

Contact

JIIOV/
RoboChallenge-CVPR-2026